System and method for determination of a digital destination based on a multi-part identifier

ABSTRACT

One general aspect includes a method, including: capturing an image of an object having a multi-part identifier displayed thereon, the multi-part identifier including a first portion and a second portion, the first portion including graphical content and the second portion including human-recognizable textual content. The method also includes based on the captured image, identifying a domain associated with the graphical content. The method also includes based on the captured image, identifying a sub-part of the domain associated with the textual content. The method also includes identifying a digital destination based on the identified domain and the identified sub-part. The method also includes performing an action based on the digital destination. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

BACKGROUND

Quick response (QR) codes and other two-dimensional barcodes can be usedon various mobile device operating systems. These devices supportUniform Resource Locator (URL) redirection, which allows QR codes tosend metadata to existing applications on the device. Many paid or freeapps are available with the ability to scan the codes and hard-link toan external URL. These codes consist of black squares arranged in asquare grid on a white background, which can be read by an imagingdevice such as a camera. As such, a user typically has no idea which URLthe QR code may redirect to upon scanning the QR code with the camera.This frustrates the process for the user and makes the user less likelyto want to use the QR code for URL redirection.

The embodiments described herein solve these problems, both individuallyand collectively.

BRIEF SUMMARY

The embodiments described herein relate to capturing an image includingan object and identifying the object within the image, the object havinga multi-part identifier including a first portion and a second portion.The object can be, for example, handwritten text on a piece of paper orprinted text on a poster or flyer. The first portion may includegraphical content and the second portion may include human-recognizabletextual content. The graphical content may be used to identify a domainand the human-recognizable textual content may be used to identify asub-part of the domain. For example, the graphical content may include alogo of a social network service provider and the human-recognizabletextual content may include a username of a of a user registered withthe social network. Together, the graphical content and thehuman-recognizable textual content may be used to identify a digitaldestination and an action may be performed dependent on the identifieddigital destination.

For example, an action that can be taken may include, upon capturing andidentifying an object including the graphical content and thehuman-recognizable textual content at a sunglasses store with a userdevice, the user device may be able to employ augmented reality (AR)effects, via the device's camera) within the store to overlay sunglassessold by the store over the user's as previewed on a front facing cameraof the device. Another example of an action that can be taken includesopening a payment application and pre-filling the recipient info for apayment based on the identified digital destination.

The combination of the graphical content and the human-recognizablecontent may provide advantages over traditional QR codes. First, asmentioned above, the combination of the graphical content and thehuman-recognizable content provides for a 1:1 mapping of the object to adigital destination. Second, the combination of the graphical contentand the human-recognizable content is generally human-readable andunderstandable, which can give the user of the camera some idea of theaction that will be initiated based on the identified digitaldestination upon capturing and analyzing the object. Identification ofthe object can be trained by a machine learning model. Morespecifically, a convolutional neural network model can be trained toestimate a position and size of the graphical content and thehuman-recognizable content.

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. Onegeneral aspect includes a method, including: capturing an image of anobject having a multi-part identifier displayed thereon, the multi-partidentifier including a first portion and a second portion, the firstportion including graphical content and the second portion includinghuman-recognizable textual content. The method also includes based onthe captured image, identifying a domain associated with the graphicalcontent. The method also includes based on the captured image,identifying a sub-part of the domain associated with the textualcontent. The method also includes identifying a digital destinationbased on the identified domain and the identified sub-part. The methodalso includes performing an action based on the digital destination.Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. Themethod where performing the action includes displaying contentassociated with the digital destination. The method where performing theaction includes executing an application based on data stored at thedigital destination. The method where identifying the sub-part of thedomain associated with the textual content includes decoding the textualcontent. The method where identifying the domain associated with thegraphical content includes providing the graphical content as an inputto a machine learning model, where, in response to the input, themachine learning model outputs a class identifying the domain. Themethod where the machine learning model is a convolutional neuralnetwork (CNN). The method where the object includes at least one of aposter, billboard, sign, handwritten content, digital content, orreceipt. Implementations of the described techniques may includehardware, a method or process, or computer software on acomputer-accessible medium.

One general aspect includes a system, including: a processor; and anon-transitory computer readable medium coupled the processor, thecomputer readable medium including code, executable by the processor,for implementing a method including: The system also includes capturingan image of an object having a multi-part identifier displayed thereon,the multi-part identifier including a first portion and a secondportion, the first portion including graphical content and the secondportion including human-recognizable textual content. The system alsoincludes based on the captured image, identifying a domain associatedwith the graphical content. The system also includes based on thecaptured image, identifying a sub-part of the domain associated with thetextual content. The system also includes identifying a digitaldestination based on the identified domain and the identified sub-part.The system also includes performing an action based on the digitaldestination. Other embodiments of this aspect include correspondingcomputer systems, apparatus, and computer programs recorded on one ormore computer storage devices, each configured to perform the actions ofthe methods.

Implementations may include one or more of the following features. Thesystem where performing the action includes displaying contentassociated with the digital destination. The system where performing theaction includes executing an application based on data stored at thedigital destination. The system where identifying the sub-part of thedomain associated with the textual content includes decoding the textualcontent. The system where identifying the domain associated with thegraphical content includes providing the graphical content as an inputto a machine learning model, where, in response to the input, themachine learning model outputs a class identifying the domain. Thesystem where the machine learning model is a convolutional neuralnetwork (CNN). The system where the object includes at least one of aposter, billboard, sign, handwritten content, digital content, orreceipt. Implementations of the described techniques may includehardware, a method or process, or computer software on acomputer-accessible medium.

One general aspect includes one or more non-transitory computer-readablemedia storing computer-executable instructions that, when executed,cause one or more computing devices to, including: capture an image ofan object having a multi-part identifier displayed thereon, themulti-part identifier including a first portion and a second portion,the first portion including graphical content and the second portionincluding human-recognizable textual content. The one or morenon-transitory computer-readable media also includes based on thecaptured image, identify a domain associated with the graphical content.The one or more non-transitory computer-readable media also includesbased on the captured image, identify a sub-part of the domainassociated with the textual content. The one or more non-transitorycomputer-readable media also includes identify a digital destinationbased on the identified domain and the identified sub-part. The one ormore non-transitory computer-readable media also includes perform anaction based on the digital destination. Other embodiments of thisaspect include corresponding computer systems, apparatus, and computerprograms recorded on one or more computer storage devices, eachconfigured to perform the actions of the methods.

Implementations may include one or more of the following features. Theone or more non-transitory computer-readable media where performing theaction includes at least one of displaying content associated with thedigital destination or executing an application based on data stored atthe digital destination. The one or more non-transitorycomputer-readable media where identifying the sub-part of the domainassociated with the textual content includes decoding the textualcontent. The one or more non-transitory computer-readable media whereidentifying the domain associated with the graphical content includesproviding the graphical content as an input to a machine learning model,where, in response to the input, the machine learning model outputs aclass identifying the domain. The one or more non-transitorycomputer-readable media where the machine learning model is aconvolutional neural network (CNN). The one or more non-transitorycomputer-readable media where the object includes at least one of aposter, billboard, sign, handwritten content, digital content, orreceipt. Implementations of the described techniques may includehardware, a method or process, or computer software on acomputer-accessible medium.

One general aspect includes a method, including: capturing an image ofan object having a multi-part identifier displayed thereon, themulti-part identifier including a first portion and a second portion,the first portion including graphical content and the second portionincluding human-recognizable textual content. The method also includesbased on the captured image, identifying a domain associated with thegraphical content. The method also includes based on the captured image,identifying a sub-part of the domain associated with the textualcontent. The method also includes identifying a digital destinationbased on the identified domain and the identified sub-part. The methodalso includes performing an action based on the digital destination.Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. Themethod where the textual content is completely or in parts non-static.The method may also include optionally, where the textual content isdisplayed on a display. The method may also include optionally, wherethe textual content changes before, during and/or after the imagecapturing. The method where performing the action includes displayingcontent associated with the digital destination. The method of any to23, where performing the action includes executing an application basedon data stored at the digital destination. The method of any to 24,where performing the action includes executing an application andperforming input to the application based on data read from the textualcontent. The method of any to 25, where identifying the sub-part of thedomain associated with the textual content includes decoding the textualcontent. The method of any to 26, where identifying the domainassociated with the graphical content includes providing the graphicalcontent as an input to a machine learning model, where, in response tothe input, the machine learning model outputs a class identifying thedomain. The method may also include optionally, where the machinelearning model is a convolutional neural network (CNN). The method ofany to 27, where the object includes at least one of a poster,billboard, sign, handwritten content, digital content, or receipt.Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

One general aspect includes a system, including: a processor; and anon-transitory computer readable medium coupled the processor, thecomputer readable medium including code, executable by the processor,for implementing a method including: The system also includes capturingan image of an object having a multi-part identifier displayed thereon,the multi-part identifier including a first portion and a secondportion, the first portion including graphical content and the secondportion including human-recognizable textual content. The system alsoincludes based on the captured image, identifying a domain associatedwith the graphical content. The system also includes based on thecaptured image, identifying a sub-part of the domain associated with thetextual content. The system also includes identifying a digitaldestination based on the identified domain and the identified sub-part.The system also includes performing an action based on the digitaldestination. Other embodiments of this aspect include correspondingcomputer systems, apparatus, and computer programs recorded on one ormore computer storage devices, each configured to perform the actions ofthe methods.

Implementations may include one or more of the following features. Thesystem where the textual content is completely or in parts non-static.The system may also include optionally, where the textual content isdisplayed on a display. The system may also include optionally, wherethe textual content changes before, during and/or after the imagecapturing. The system where performing the action includes displayingcontent associated with the digital destination. The system of any to31, where performing the action includes executing an application basedon data stored at the digital destination. The system of any to 32,where performing the action includes executing an application andperforming input to the application based on data read from the textualcontent. The system of any to 33, where identifying the sub-part of thedomain associated with the textual content includes decoding the textualcontent. The system of any to 34, where identifying the domainassociated with the graphical content includes providing the graphicalcontent as an input to a machine learning model, where, in response tothe input, the machine learning model outputs a class identifying thedomain. The system may also include optionally, where the machinelearning model is a convolutional neural network (CNN). The system ofany to 35, where the object includes at least one of a poster,billboard, sign, handwritten content, digital content, or receipt.Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

One general aspect includes a method, including: capturing an image ofan object having a multi-part identifier displayed thereon, themulti-part identifier including a first portion and a second portion,the first portion including graphical content and the second portionincluding human-recognizable textual content. The method also includesbased on the captured image, identifying a domain associated with thegraphical content. The method also includes based on the captured image,identifying a sub-part of the domain associated with the textualcontent. The method also includes identifying a digital destinationbased on the identified domain and the identified sub-part. The methodalso includes performing an action based on the digital destination.Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. Themethod where the textual content is completely or in parts non-static.The method may also include optionally, where the textual content isdisplayed on a display. The method may also include optionally, wherethe textual content changes before, during and/or after the imagecapturing. The method where performing the action includes displayingcontent associated with the digital destination. The method of any to39, where performing the action includes executing an application basedon data stored at the digital destination. The method of any to 40,where performing the action includes executing an application andperforming input to the application based on data read from the textualcontent. The method of any to 41, where identifying the sub-part of thedomain associated with the textual content includes decoding the textualcontent. The method of any to 42, where identifying the domainassociated with the graphical content includes providing the graphicalcontent as an input to a machine learning model, where, in response tothe input, the machine learning model outputs a class identifying thedomain. The method may also include optionally, where the machinelearning model is a convolutional neural network (CNN). The method ofany to 43, where the object includes at least one of a poster,billboard, sign, handwritten content, digital content, or receipt.Implementations of the described techniques may include hardware, amethod or process, or computer software on a computer-accessible medium.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the disclosure are illustrated by way of example. In theaccompanying figures, like reference numbers indicate similar elements.

FIG. 1 illustrates a simplified diagram of a mobile device, according tosome embodiments.

FIG. 2 is a flowchart illustrating an exemplary method for identifying adigital destination and performing an action based on the digitaldestination.

FIG. 3 illustrates an exemplary object having a multi-part identifierdisplayed thereon, according to some embodiments.

FIG. 4A illustrates a mobile device capturing an image of an object,according to some embodiments.

FIG. 4B illustrates a social network service application displayed on amobile device, according to some embodiments.

FIG. 4C illustrates a traditional point-of-sale (PoS) terminal with amulti-part identifier affixed thereon, according to some embodiments.

FIG. 4D illustrates a mobile payment application being displayed withina user interface of a display of a mobile device, according to someembodiments.

FIG. 5 illustrates an example of a computing system in which one or moreembodiments may be implemented.

DETAILED DESCRIPTION

Several illustrative embodiments will now be described with respect tothe accompanying drawings, which form a part hereof. While particularembodiments, in which one or more aspects of the disclosure may beimplemented, are described below, other embodiments may be used andvarious modifications may be made without departing from the scope ofthe disclosure or the spirit of the appended claims.

FIG. 1 illustrates a simplified diagram of a mobile device 100 that mayincorporate one or more embodiments. Mobile device 100 may include aprocessor 110, microphone 120, display 130, input device 140, speaker150, memory 160, action database 170, camera 180, and computer-readablemedium 190.

Processor 110 may be any general-purpose processor operable to carry outinstructions on the mobile device 100. The processor 110 is coupled toother units of the device 100 including microphone 120, display 130,input device 140, speaker 150, memory 160, action database 170, camera180, and computer-readable medium 190.

Microphone 120 may be any device that converts a sound input to anelectrical signal. The microphone 120 may capture a user's voice or anyother sound in a proximity to the mobile device 100.

Display 130 may be any device that displays information to a user.Examples may include an LCD screen, CRT monitor, or seven-segmentdisplay. In some embodiments, display 130 may be a touchscreen displaycapable of receiving input for interaction with a camera applicationexecuting on the device 100.

Input device 140 may be any device that accepts input from a user.Examples may include a keyboard, keypad, mouse, or touch input. In someembodiments, display 130 may also function as input device 140.

Speaker 150 may be any device that outputs sound to a user. Examples mayinclude a built-in speaker or any other device that produces sound inresponse to an electrical audio signal.

Memory 160 may be any magnetic, electronic, or optical memory. Anexample of memory 160 may be dynamic random access memory (DRAM).

Action database 170 may store information pertaining to one or moreactions that can be performed by the processor 110 in response toidentifying a digital destination based on an identified domain andidentified sub-part from graphical content and textual content,respectively, that is captured in an image.

Camera 180 may be configured to capture one or more images via a lens182 located on the body of mobile device 100. The lens 182 may be a partof the camera 180 subsystem. The captured images may be still images orvideo images. The camera 180 may include a CMOS image sensor to capturethe images. Various applications running on processor 110 may haveaccess to camera 180 to capture images. It can be appreciated thatcamera 180 can continuously capture images without the images actuallybeing stored within device 100. Captured images may also be referred toas image frames.

Camera 180 may also include image sensor 184. Image sensor 184 may be asensor that detects and conveys information that constitutes an image.It may do so by converting the variable attenuation of light waves (asthey pass through or reflect off objects) into signals, small bursts ofcurrent that convey the information. The waves can be light or otherelectromagnetic radiation. Image sensors are used in electronic imagingdevices of both analog and digital types. For example, when open, lens182 may allow light to shine through to the image sensor 184. Imagesensor 184 may capture the light through the lens 182 and convert thelight to an electronic signal that represents the image.

Computer-readable medium 190 may be any magnetic, electronic, optical,or other computer-readable storage medium. Computer-readable storagemedium 190 may store computer-readable code comprising code subsystems,including image capture subsystem 190 a, graphical content recognitionsubsystem 190 b, textual content recognition subsystem 190 c, digitaldestination determination subsystem 190 d, and action subsystem 190 e.

Image capture subsystem 190 a, contains code that, when executed byprocessor 110, may capture an image using the lens 182 and image sensor184 of the camera 180 on the mobile device 100. The captured image maybe of a field-of-view of the camera 180 positioned behind the rear of anouter body of the mobile device 100. The image capture subsystem 190 amay be executed by the processor 110 when, for example, a user launchesa camera application on the mobile device 100. The image capturesubsystem 190 a may capture a frame or multiple frames in real-time ofthe field-of-view of the camera 180. In some embodiments, thefield-of-view of the camera 180 may include one or more objects having amulti-part identifier displayed thereon. The multi-part identifier mayinclude a first portion and a second the portion, the first portionincluding graphical content and the second portion includinghuman-recognizable textual content.

Graphical content recognition subsystem 190 b contains code that, whenexecuted by processor 110, may analyze an object within the imagecaptured by the image capture subsystem 190 a. The graphical contentrecognition subsystem 190 b may analyze the object to locate andidentify graphical content that is part of a multi-part identifierdisplayed on the object. The graphical content may be located andidentified by employing a machine learning model that has been trainedusing various images of graphical content having different shapes,sizes, and fonts. For example, the graphical content may be a logoassociated with a social network service, such as the letter “f”. Insome embodiments, the logo may be qualified as identifiable graphicalcontent by annotating the logo. For example, the “f” logo may bequalified by brackets around the letter, such as “[f]”. The machinelearning model may receive as an input the image captured by imagecapture subsystem 190 a and may output a class identifying whether anobject in an image contains graphical content that is part of amulti-part identifier. The graphical content recognition subsystem 190 bmay output the identified graphical content to the digital destinationdetermination subsystem 190 d.

Textual content recognition subsystem 190 c contains code that, whenexecuted by processor 110, may analyze the object within the imagecaptured by the image capture subsystem 190 a. The textual contentrecognition subsystem 190 c may analyze the object to locate andidentify textual content that is part of the multi-part identifierdisplayed on the object. The textual content may be located andidentified by employing the machine learning model described above or byusing an algorithm that parses or decodes the textual content toidentify the characters of the of the textual content using opticalcharacter recognition (OCR). For example, the textual content may betext that reads “Liam_Neeson”. The textual content recognition subsystem190 c may identify the characters in the text “Liam_Neeson” and providethe characters of the textual content to the digital destinationdetermination subsystem 190 d. In some embodiments, the textual contentmay be completely or in parts non-static. The textual content may be,for example, displayed on a display. In some embodiments, the textualcontent may change before, during, and/or after the image is captured.For example, the textual content may be a price displayed on a cashregister display, the display may change each time an additional item isadded to the subtotal or after sales tax is added to the subtotal.

The digital destination determination subsystem 190 d contains codethat, when executed by processor 110, identifies a domain based on thegraphical content output by the graphical content recognition subsystem190 b and identifies a sub-part of the domain based on the textualcontent output by the textual content recognition subsystem 190 c. Forexample, the digital destination determination subsystem 190 d mayidentify a specific domain that is associated with the identifiedgraphical content. For example, if the graphical content portrays an“[f]”, the digital destination determination subsystem 190 d model mayidentify that the domain associated with this graphical content isFacebook. In some embodiments, the identification of the domain based onthe graphical content may be accomplished by querying a database ofstored domains associated with different related graphical contents. Insome embodiments, the identification of the sub-part of the domain basedon the textual content may be based on the characters of the textualcontent output by the textual content recognition subsystem 190 c. Forexample, the digital destination determination subsystem 190 d mayidentify the domain as Facebook, based on the graphical content, and thesub-part of the domain as “Liam_Neeson” based on the textual content.

Upon identifying the domain and sub-domain, the digital destinationdetermination subsystem 190 d may identify or determine a digitaldestination based on the identified domain and identified sub-part ofthe domain. The combination of the domain and the sub-part of the domainmay both make up the digital destination. For example, if the domain isa domain for a social network service, such as the “[f]” exampleprovided above, and the sub-part of the domain is a user profile addresswithin the social network service, such as “Liam_Neeson,” the digitaldestination may be the profile page of the user “Liam_Neeson.” Inanother example, if the domain is a domain for a payment service, andthe sub-part of the domain is a username for a user registered on thepayment service, the digital destination may be payment profile for theregistered user. In yet another example, if the domain is a domain formobile application store, and the sub-part of the domain is a name of avirtual game, the digital destination may be a download page for thevirtual game on the mobile application store. These are just a fewexamples of digital domains, in addition to further examples describedbelow.

In some embodiments, the combination of the domain and the sub-part ofthe domain may be used for URL schemes for inter-app communications. Forexample, the domain may be a domain for a third-party application thatexecutes on the mobile device. The sub-part of the domain may be aparticular URL accessible by the third-party application. Accordingly,the digital destination may be the URL opened by the third-partyapplication.

The action subsystem 190 e contains code that, when executed byprocessor 110, performs an action based on the digital destination. Insome embodiments, performing the action may include displaying contentassociated with the digital destination or executing an applicationbased on data stored at the digital destination. In some embodiments,the action subsystem 190 e may query the action database 170 in order todetermine the appropriate action to perform based on the digitaldestination. The action database 170 may store a list of actions thatcan be performed dependent on various domains and various sub-parts ofthe domain. Referencing the examples above, if the domain is a domainfor a social network service, and the sub-part of the domain is a userprofile address within the social network service, the action subsystem190 e may launch a web browser or mobile application for the socialnetwork service and direct the web browser or mobile application to theprofile page of the user. In another example, if the domain is a domainfor a payment service, and the sub-part of the domain is a username fora user registered on the payment service, the action subsystem 190 e maylaunch a payment application local to the mobile device 100 and pre-fillpayment information pertaining to the user registered on the paymentservice and a payment amount. In another example, if the domain is adomain for mobile application store, and the sub-part of the domain is aname of a virtual game, the action subsystem 190 e may launch anapplication for the mobile application store and direct the applicationto the download page for the virtual game.

In some embodiments, performing the action includes executing anapplication and performing input to the application based on data readfrom the textual content. For example, the textual content, togetherwith the domain, may be indicative of a digital destination pertainingto a retailer's checkout page within a third-party payment application.Performing the action may include launching the third-party applicationon the mobile device and directing the application to the retailer'scheckout page.

In some embodiments, the functionality of the graphical contentrecognition subsystem 190 b, textual content recognition subsystem 190c, and digital destination determination subsystem 190 d may beimplemented on a server computer communicatively coupled to the mobiledevice 100. Upon capturing an image, via the image capture subsystem 190a, the mobile device 100 may transmit via a transceiver (not shown) thecaptured image for processing and analysis to the server computer. Theserver computer may identify the graphical content and textual contentwithin the image, and determine a digital destination based thereon. Theserver computer may return identity of the digital destination to themobile device 100, and the action subsystem 190 e may perform anappropriate action on the digital destination.

The embodiments described above provide numerous advantages overtraditional QR codes or bar codes. By recognizing graphical contentinstead of a binary grid of squares, a user performing the image capturemay have a better understanding of the general action that may beperformed on the digital destination. For example, if the graphicalcontent is an “[f]”, as illustrated in the examples above, the userperforming the image capture may have a general idea that the digitaldestination may be within the Facebook domain. Additionally, by using aclassifier to identify the domain associated with the graphical content,the graphical content does not need to be a perfect reproduced each timeit is printed or drawn on an object. Variations in the printing ordrawing of the graphical content may be accounted for by using themachine learning model which may still be able to identify the domainassociated with the graphical content, even though the graphical contentmay vary on different objects. In contrast, traditional QR codes or barcodes require square or location in the binary grid to be properlycaptured and decoded to function properly. Further, as described above,the embodiments described herein are capable of identifying a domainthat is associated with handwritten or drawn of graphical content.

In some embodiments, the graphical content may act as an anchor for theidentification of the digital destination and the textual content maynot be inherently static. For example, the textual content may betime-based and change over time, such as a one-time password or a tokenor URL.

FIG. 2 is a flowchart 200 illustrating an exemplary method foridentifying a digital destination and performing an action based on thedigital destination. The method beings at step 202, for example, when auser launches a camera application or other application on the mobiledevice 100 that has an embedded camera function. At step 204, an imageor continuous series of images is captured by the application and via acamera of a mobile device. For example, the image capture subsystem 190a may capture a frame or multiple frames in real-time of a field-of-viewof the camera 180. The image may include an object having a multi-partidentifier. For example, the object may be a poster, billboard, sign, apiece of paper, digital content on a display, or a receipt. Themulti-part identifier may include a first portion and a second portion.The first portion of the multi-part identifier may include graphicalcontent and the second portion of the multi-part identifier may includehuman-recognizable textual content.

For example, the multi-part identifier may read “*sN*john.smith”, where“sN” is the first portion and “john.smith” is the second portion. The“sN” may be graphically printed or written on the object. For example,the “sN” may be a stylized logo of a social network service. The secondportion, “john.smith”, may be human-recognizable textual content that ishand-written or printed using a standardized font.

At step 206, after the image or continuous series of images is capturedby the application via the camera of the mobile device, a domainassociated with the graphical content may be identified. For example,the graphical content recognition subsystem 190 b may analyze the objectto locate and identify graphical content that is part of a multi-partidentifier displayed on the object. The graphical content may be locatedand identified by employing a machine learning model that has beentrained using various images of graphical content having differentshapes, sizes, and fonts. In some embodiments the machine learning modelmay be a convolutional neural network (CNN). For example, the graphicalcontent may be a logo associated with a social network service, such asthe “*sN*”. In some embodiments, the logo may be qualified asidentifiable graphical content by annotating the logo. For example, the“sN” logo may be qualified by stars around the letter, such as “*sN*”.The machine learning model may receive as an input the image captured byimage capture subsystem 190 a and may output a class identifying whetheran object in an image contains graphical content that is part of amulti-part identifier.

At step 208, after the domain associated with the graphical content isidentified, a sub-part of the domain associated with the textual contentmay be identified. For example, the textual content recognitionsubsystem 190 c may analyze the object to locate and identify textualcontent that is part of the multi-part identifier displayed on theobject. The textual content may be located and identified by employingthe machine learning model described above or by using an algorithm thatparses or decodes the textual content to identify the characters of theof the textual content using optical character recognition (OCR). Forexample, as described above, the textual content may be text that reads“john.smith”. The textual content recognition subsystem 190 c mayidentify the characters in the text “john.smith” and provide thecharacters of the textual content to the digital destinationdetermination subsystem 190 d.

At step 210, after the sub-part of the domain associated with thetextual content is identified, an action may be performed based on thedigital destination. In some embodiments, performing the action mayinclude displaying content associated with the digital destination orexecuting an application based on data stored at the digitaldestination. For example, the action subsystem 190 e may query theaction database 170 in order to determine the appropriate action toperform based on the digital destination. Some examples of actions thatmay be performed based on the digital destination are described abovewith respect to the description of the action subsystem 190 e.

FIG. 3 illustrates an exemplary object 310 having a multi-partidentifier displayed thereon, according to some embodiments. In thisexample, the object 310 is a compact disc (CD). The object 310 has amulti-part identifier displayed thereon which includes a first portion320 including graphical content and a second portion 330 includinghuman-recognizable textual content. The first portion 320 includesgraphical content in the form of a logo, shown as “f”. The logo may be alogo associated with a social network service. The logo may be, forexample, printed on the object 310, handwritten on the object 310, asticker placed on the object 310, etc. Additionally, the square thatencapsulates the “f” may serve as a qualifier for the graphical content,as described above.

The second portion includes human-recognizable textual content thatreads “SallyWilliams”. Again, the human-recognizable textual content maybe, for example, printed on the object 310, handwritten on the object310, a sticker placed on the object 310, etc.

As described above, an image may be captured of the object 310 via acamera application on the mobile device 100. The captured image may beanalyzed to identify a domain based on the graphical content of thefirst portion 320. For example, the graphical content recognitionsubsystem 190 b may analyze the object 310 to locate and identifygraphical content that is part of the first portion 320, using theclassifier described in the examples above. Additionally, the capturedimage may also be analyzed to identify a sub-part of the domain that isbased on the human-recognizable textual content of the second portion330. For example, the textual content recognition subsystem 190 c mayanalyze the object to locate and identify textual content that is partof the multi-part identifier displayed on the object using characterparsing or decoding, described above.

In an illustrative example, a user may purchase the CD which storesmusic by a particular artist, with the multi-part identifier displayedthereon. The second user may, at his/her convenience, play the mediastored on the CD and become intrigued with the artist and wish to learnmore about the artist. The multi-part identifier may represent a link tothe artist's profile page within the social network service. The usermay open a camera application on his/her mobile device and capture animage or a series of images the CD. The image(s) may be analyzed by thegraphical content recognition subsystem 190 b and the textual contentrecognition subsystem 190 c to determine the domain and sub-part of thedomain, in accordance with the examples above. For example, the domainmay be identified as the social network service, affiliated with thelogo “f” and the sub-part of the domain may be identified as“SallyWilliams.” Upon identifying the domain and sub-part of the domain,a digital destination may be identified based on the domain and sub-partof the domain by the digital destination determination subsystem 190 d.For example, the digital destination may be identified as SallyWilliam's profile page within the social network service affiliated withthe logo “f”. The action subsystem 190 e may then determine and performan action based on the digital destination. For example, the actionsubsystem 190 e may associate anything identified within the “f” domainto be opened by a social network service application associated with thesocial network service. As a result, the action subsystem 190 e maylaunch the social network service application on the mobile device 100and direct the social network service application to the profile pagefor Sally William. Alternatively, if the social network serviceapplication is not installed on the mobile device 100, the actionsubsystem 190 e may instead launch a web browser application and directthe web browser application to a URL within the domain of the socialnetwork service that hosts Sally William's profile page.

FIG. 4A illustrates a mobile device 100 capturing an image of an object420, according to some embodiments. In this example, the object 420 maybe a business card with a multi-part identifier thereon. The object 420has a multi-part identifier displayed thereon which includes a firstportion 430 including graphical content and a second portion 440including human-recognizable textual content. The first portion 430includes graphical content in the form of a logo, shown as “f”. The logomay be a logo associated with a social network service. The logo may be,for example, printed on the object 420, handwritten on the object 420, asticker placed on the object 420, etc. Additionally, the brackets thatsurround the “f” may serve as a qualifier for the graphical content, asdescribed above.

The second portion includes human-recognizable textual content thatreads “Liam_Neeson”. Again, the human-recognizable textual content maybe, for example, printed on the object 420, handwritten on the object420, a sticker placed on the object 420, etc.

In an illustrative example, and similar to description with respect toFIG. 3, a user may be given the business card by an acquaintance. Atsome point, the user may wish to learn more about the acquaintance andvisit the acquaintance's profile within the social network service. Theuser may have an idea that scanning the multi-part identifier with thecamera of the mobile device 100 may result in the mobile device 100launching a social network service application and directing theapplication to Liam_Neeson's profile within the social network service,because of the graphical content that may be a well-known logoaffiliated with the social network service. The user may open a cameraapplication on the mobile device 100 and be presented with a preview ofthe field-of-view of the camera shown within a user interface 410displayed on the display of the mobile device 100.

The user may hover the mobile device 100 over the business card suchthat the business card is captured within the preview shown within theuser interface 410. Upon the graphical content recognition subsystem 190b and the textual content recognition subsystem 190 c identifying thegraphical content and textual content of the multi-part identifier, anoverlay encapsulating the multi-part identifier may be presented withinthe user interface 410. For example, the overlay may be a rectangle withdashed lines that encapsulates the multi-part identifier. The overlaymay provide the user with confirmation that the multi-part identifierwas correctly recognized by the subsystems and the user interface 410.Additionally, upon the graphical content recognition subsystem 190 b andthe textual content recognition subsystem 190 c identifying thegraphical content and textual content of the multi-part identifier, thedigital destination determination subsystem 190 d may determine adigital destination based on the identified graphical content andtextual content. Upon determining the digital destination based on theidentified graphical content and textual content, the user interface 410may present a notification 450 to the user that indicates that a digitaldestination was determined. For example, the notification 450 may be apop-up notification within the user interface that reads “DigitalDestination Deteted! Tap to continue . . . ” The user may be able tointeract with the notification 450 by providing an input gesture, suchas a tap, to select the notification 450.

Upon interacting with the notification 450, the action subsystem 190 emay perform the appropriate action on the digital destination. Forexample, as illustrated in FIG. 4B, the action subsystem 190 e maydetermine that the action to be performed on the digital destination isto open up the social network service application running on the mobiledevice 100 and direct the social network service application to displaythe profile page of Liam_Neeson. The profile page may be displayedwithin the social network service application, within the user interface410 displayed on a display of the mobile device 100. The entire processfrom scanning the multi-part identifier on the object 420 with thecamera of the mobile device 100 up to the user being able to view theprofile page within the social network service application may be donein less than a few seconds.

As illustrated, the multi-part identifier can be both useful forindividuals to use and fun for individuals to interact with. Theembodiments described herein can be employed for many use cases. Forexamples, two strangers may meet at a bar and get along with oneanother. They may wish to become friends on a social network service.Each of the individuals may scribble down on a napkin a simple logo ofthe social network service followed by their username on the socialnetwork service. For example, one of the individuals may scribble down“[f]jason17”. The next day, for example, the other individual may scanthe scribbled down text on the napkin with the camera on the mobiledevice 100 and be presented with jason17's social network serviceprofile shown on the mobile device 100. In another example, anindividual may wish to purchase an item from a seller who does not havea point-of-sale (PoS) terminal. Instead, the seller may have a signposted at their checkout counter with graphical content that resembles alogo affiliated with a mobile payment service followed by the seller'spayment account number or username. Upon checkout, the individualwishing to purchase the item may scan the sign shown at the seller'scheckout counter on the mobile device 100. In accordance with thedescription above, the digital destination may be determined to be amobile payment application residing on the individual's device. Theaction subsystem 190 e may launch the mobile payment application on themobile device 100 and direct the mobile payment application to display astore profile for the seller, where the individual may quickly be ableto enter a transaction amount and submit a mobile payment to the sellerfor the item(s).

In some embodiments, an user may be provided with a receipt from aseller. In order to be discrete, the receipt may only include amulti-part identifier and no transaction specific information as maynormally be found on a receipt. For example, the multi-part identifieron the receipt may display “[r]38195810”. Upon scanning the multi-partidentifier with a camera of the mobile device 100, the digitaldestination may be determined to be a web page where the full receiptfor the transaction can be viewed. The action subsystem 190 e may launcha web browser application on the mobile device 100 and direct the webbrowser application to a web page where the receipt is displayed to theuser. For example, the graphical content (“[r]”) may be associated witha domain for a receipt tracking service, and the human-recognizabletextual content (38195810) may be associated with the receipt number.The web page opened may be, for example,http://trackmyreceipts.com/38195810.

FIG. 4C illustrates a traditional point-of-sale (PoS) terminal with amulti-part identifier affixed thereon, according to some embodiments.The embodiments described herein may also be advantageous for sellerswho employ traditional POS terminals that do not accept credit cardpayments, such as a traditional cash register system 460 depicted inFIG. 5. The seller may affix a sticker 470 having a multi-partidentifier displayed thereon to the cash register 460. The multi-partidentifier may read “[pP]dessertlife”. Upon checkout, a user wishing topurchase goods at this seller may scan the sticker 470 and also capturethe cash register 460 in the image frame. In accordance with thedescription above, upon scanning the sticker 470 and capturing the cashregister 460 in the image frame, the digital destination may bedetermined to be a mobile payment application affiliated with the mobilepayment service identified by the logo (e.g., graphical content) “pP”.The action subsystem 190 e may launch a mobile payment application onthe user's mobile device 100 and direct the mobile payment applicationto a payment page for the seller, identified by textual content“dessertlife”. In addition, action subsystem 190 e may pre-fill thetransaction amount of $9.83 in the payment page for the seller. This maybe accomplished by the action subsystem 190 e recognizing the charactersshown on the display on the cash register 460. As such, the amount of$9.83 may be pre-filled on the seller's payment page and all the usermay simply need to do to pay for the goods is hit a payment confirmationbutton displayed within the mobile payment application to send thepayment to the seller. An example of the mobile payment applicationbeing displayed within a user interface of a display of the mobiledevice 100 is shown in FIG. 4D.

FIG. 5 illustrates an example of a computing system in which one or moreembodiments may be implemented. A computer system as illustrated in FIG.5 may be incorporated as part of the above described computerizeddevice. For example, computer system 500 can represent some of thecomponents of a television, a computing device, a server, a desktop, aworkstation, a control or interaction system in an automobile, a tablet,a netbook or any other suitable computing system. A computing device maybe any computing device with an image capture device or input sensoryunit and a user output device. An image capture device or input sensoryunit may be a camera device. A user output device may be a display unit.Examples of a computing device include but are not limited to video gameconsoles, tablets, smart phones and any other hand-held devices. FIG. 5provides a schematic illustration of one embodiment of a computer system500 that can perform the methods provided by various other embodiments,as described herein, and/or can function as the host computer system, aremote kiosk/terminal, a point-of-sale device, a telephonic ornavigation or multimedia interface in an automobile, a computing device,a set-top box, a table computer and/or a computer system. FIG. 5 ismeant only to provide a generalized illustration of various components,any or all of which may be utilized as appropriate. FIG. 5, therefore,broadly illustrates how individual system elements may be implemented ina relatively separated or relatively more integrated manner. In someembodiments, elements computer system 500 may be used to implementfunctionality of the mobile device 100 in FIG. 1.

The computer system 500 is shown comprising hardware elements that canbe electrically coupled via a bus 502 (or may otherwise be incommunication, as appropriate). The hardware elements may include one ormore processors 504, including without limitation one or moregeneral-purpose processors and/or one or more special-purpose processors(such as digital signal processing chips, graphics accelerationprocessors, and/or the like); one or more input devices 508, which caninclude without limitation one or more cameras, sensors, a mouse, akeyboard, a microphone configured to detect ultrasound or other sounds,and/or the like; and one or more output devices 510, which can includewithout limitation a display unit such as the device used in embodimentsof the invention, a printer and/or the like.

In some implementations of the embodiments of the invention, variousinput devices 508 and output devices 510 may be embedded into interfacessuch as display devices, tables, floors, walls, and window screens.Furthermore, input devices 508 and output devices 510 coupled to theprocessors may form multi-dimensional tracking systems.

The computer system 500 may further include (and/or be in communicationwith) one or more non-transitory storage devices 506, which cancomprise, without limitation, local and/or network accessible storage,and/or can include, without limitation, a disk drive, a drive array, anoptical storage device, a solid-state storage device such as a randomaccess memory (“RAM”) and/or a read-only memory (“ROM”), which can beprogrammable, flash-updateable and/or the like. Such storage devices maybe configured to implement any appropriate data storage, includingwithout limitation, various file systems, database structures, and/orthe like.

The computer system 500 might also include a communications subsystem512, which can include without limitation a modem, a network card(wireless or wired), an infrared communication device, a wirelesscommunication device and/or chipset (such as a Bluetooth™ device, an802.11 device, a Wi-Fi device, a WiMax device, cellular communicationfacilities, etc.), and/or the like. The communications subsystem 512 maypermit data to be exchanged with a network, other computer systems,and/or any other devices described herein. In many embodiments, thecomputer system 500 will further comprise a non-transitory workingmemory 518, which can include a RAM or ROM device, as described above.

The computer system 500 also can comprise software elements, shown asbeing currently located within the working memory 518, including anoperating system 514, device drivers, executable libraries, and/or othercode, such as one or more application programs 516, which may comprisecomputer programs provided by various embodiments, and/or may bedesigned to implement methods, and/or configure systems, provided byother embodiments, as described herein. Merely by way of example, one ormore procedures described with respect to the method(s) discussed abovemight be implemented as code and/or instructions executable by acomputer (and/or a processor within a computer); in an aspect, then,such code and/or instructions can be used to configure and/or adapt ageneral purpose computer (or other device) to perform one or moreoperations in accordance with the described methods.

A set of these instructions and/or code might be stored on acomputer-readable storage medium, such as the storage device(s) 506described above. In some cases, the storage medium might be incorporatedwithin a computer system, such as computer system 500. In otherembodiments, the storage medium might be separate from a computer system(e.g., a removable medium, such as a compact disc), and/or provided inan installation package, such that the storage medium can be used toprogram, configure and/or adapt a general purpose computer with theinstructions/code stored thereon. These instructions might take the formof executable code, which is executable by the computer system 500and/or might take the form of source and/or installable code, which,upon compilation and/or installation on the computer system 500 (e.g.,using any of a variety of generally available compilers, installationprograms, compression/decompression utilities, etc.) then takes the formof executable code.

Substantial variations may be made in accordance with specificrequirements. For example, customized hardware might also be used,and/or particular elements might be implemented in hardware, software(including portable software, such as applets, etc.), or both. Further,connection to other computing devices such as network input/outputdevices may be employed. In some embodiments, one or more elements ofthe computer system 500 may be omitted or may be implemented separatefrom the illustrated system. For example, the processor 504 and/or otherelements may be implemented separate from the input device 508. In oneembodiment, the processor is configured to receive images from one ormore cameras that are separately implemented. In some embodiments,elements in addition to those illustrated in FIG. 5 may be included inthe computer system 500.

Some embodiments may employ a computer system (such as the computersystem 500) to perform methods in accordance with the disclosure. Forexample, some or all of the procedures of the described methods may beperformed by the computer system 500 in response to processor 504executing one or more sequences of one or more instructions (which mightbe incorporated into the operating system 514 and/or other code, such asan application program 516) contained in the working memory 518. Suchinstructions may be read into the working memory 518 from anothercomputer-readable medium, such as one or more of the storage device(s)506. Merely by way of example, execution of the sequences ofinstructions contained in the working memory 518 might cause theprocessor(s) 504 to perform one or more procedures of the methodsdescribed herein.

The terms “machine-readable medium” and “computer-readable medium,” asused herein, refer to any medium that participates in providing datathat causes a machine to operate in a specific fashion. In someembodiments implemented using the computer system 500, variouscomputer-readable media might be involved in providing instructions/codeto processor(s) 504 for execution and/or might be used to store and/orcarry such instructions/code (e.g., as signals). In manyimplementations, a computer-readable medium is a physical and/ortangible storage medium. Such a medium may take many forms, includingbut not limited to, non-volatile media, volatile media, and transmissionmedia. Non-volatile media include, for example, optical and/or magneticdisks, such as the storage device(s) 506. Volatile media include,without limitation, dynamic memory, such as the working memory 518.Transmission media include, without limitation, coaxial cables, copperwire and fiber optics, including the wires that comprise the bus 502, aswell as the various components of the communications subsystem 512(and/or the media by which the communications subsystem 512 providescommunication with other devices). Hence, transmission media can alsotake the form of waves (including without limitation radio, acousticand/or light waves, such as those generated during radio-wave andinfrared data communications).

Common forms of physical and/or tangible computer-readable mediainclude, for example, a floppy disk, a flexible disk, hard disk,magnetic tape, or any other magnetic medium, a CD-ROM, any other opticalmedium, punchcards, papertape, any other physical medium with patternsof holes, a RAM, a PROM, EPROM, a FLASH-EPROM, any other memory chip orcartridge, a carrier wave as described hereinafter, or any other mediumfrom which a computer can read instructions and/or code.

Various forms of computer-readable media may be involved in carrying oneor more sequences of one or more instructions to the processor(s) 504for execution. Merely by way of example, the instructions may initiallybe carried on a magnetic disk and/or optical disc of a remote computer.A remote computer might load the instructions into its dynamic memoryand send the instructions as signals over a transmission medium to bereceived and/or executed by the computer system 500. These signals,which might be in the form of electromagnetic signals, acoustic signals,optical signals and/or the like, are all examples of carrier waves onwhich instructions can be encoded, in accordance with variousembodiments of the invention.

The communications subsystem 512 (and/or components thereof) generallywill receive the signals, and the bus 502 then might carry the signals(and/or the data, instructions, etc. carried by the signals) to theworking memory 518, from which the processor(s) 504 retrieves andexecutes the instructions. The instructions received by the workingmemory 518 may optionally be stored on a non-transitory storage device506 either before or after execution by the processor(s) 504.

The methods, systems, and devices discussed above are examples. Variousembodiments may omit, substitute, or add various procedures orcomponents as appropriate. For instance, in alternative configurations,the methods described may be performed in an order different from thatdescribed, and/or various stages may be added, omitted, and/or combined.Also, features described with respect to certain embodiments may becombined in various other embodiments. Different aspects and elements ofthe embodiments may be combined in a similar manner. Also, technologyevolves and, thus, many of the elements are examples that do not limitthe scope of the disclosure to those specific examples.

Specific details are given in the description to provide a thoroughunderstanding of the embodiments. However, embodiments may be practicedwithout these specific details. For example, well-known circuits,processes, algorithms, structures, and techniques have been shownwithout unnecessary detail in order to avoid obscuring the embodiments.This description provides example embodiments only, and is not intendedto limit the scope, applicability, or configuration of the invention.Rather, the preceding description of the embodiments will provide thoseskilled in the art with an enabling description for implementingembodiments of the invention. Various changes may be made in thefunction and arrangement of elements without departing from the spiritand scope of the invention.

Also, some embodiments are described as processes depicted as flowdiagrams or block diagrams. Although each may describe the operations asa sequential process, many of the operations can be performed inparallel or concurrently. In addition, the order of the operations maybe rearranged. A process may have additional steps not included in thefigures. Furthermore, embodiments of the methods may be implemented byhardware, software, firmware, middleware, microcode, hardwaredescription languages, or any combination thereof. When implemented insoftware, firmware, middleware, or microcode, the program code or codesegments to perform the associated tasks may be stored in acomputer-readable medium such as a storage medium. Processors mayperform the associated tasks. Thus, in the description above, functionsor methods that are described as being performed by the computer systemmay be performed by a processor—for example, the processor504—configured to perform the functions or methods. Further, suchfunctions or methods may be performed by a processor executinginstructions stored on one or more computer readable media.

Having described several embodiments, various modifications, alternativeconstructions, and equivalents may be used without departing from thespirit of the disclosure. For example, the above elements may merely bea component of a larger system, wherein other rules may take precedenceover or otherwise modify the application of the invention. Also, anumber of steps may be undertaken before, during, or after the aboveelements are considered. Accordingly, the above description does notlimit the scope of the disclosure.

Various examples have been described. These and other examples arewithin the scope of the following claims.

What is claimed is:
 1. A method, comprising: capturing an image of anobject having a multi-part identifier displayed thereon, the multi-partidentifier comprising a first portion and a second portion, the firstportion comprising graphical content and the second portion comprisinghuman-recognizable textual content; identifying a domain based on thegraphical content, wherein the domain represents a network having aplurality of network addresses; identifying a sub-part of the domainbased on the textual content, wherein the sub-part of the domainrepresents a particular network address in the plurality of networkaddresses; identifying a digital destination based on the identifieddomain and the identified sub-part, the digital destinationcorresponding to the particular network address represented by thesub-part of the domain; and performing an action based on the digitaldestination.
 2. The method of claim 1, wherein performing the actioncomprises displaying content associated with the digital destination. 3.The method of claim 1, wherein performing the action comprises executingan application based on data stored at the digital destination.
 4. Themethod of claim 1, wherein identifying the sub-part of the domain basedon the textual content comprises decoding the textual content.
 5. Themethod of claim 1, wherein identifying the domain based on the graphicalcontent comprises providing the graphical content as an input to amachine learning model, wherein, in response to the input, the machinelearning model outputs a class identifying the domain.
 6. The method ofclaim 5, wherein the machine learning model is a convolutional neuralnetwork (CNN).
 7. The method of claim 1, wherein the object comprises atleast one of a poster, billboard, sign, handwritten content, digitalcontent, or receipt.
 8. A system, comprising: a processor; and anon-transitory computer readable medium coupled to the processor, thecomputer readable medium comprising code, executable by the processor,for implementing a method comprising: capturing an image of an objecthaving a multi-part identifier displayed thereon, the multi-partidentifier comprising a first portion and a second portion, the firstportion comprising graphical content and the second portion comprisinghuman-recognizable textual content; identifying a domain based on thegraphical content, wherein the domain represents a network having aplurality of network addresses; identifying a sub-part of the domainbased on the textual content, wherein the sub-part of the domainrepresents a particular network address in the plurality of networkaddresses; identifying a digital destination based on the identifieddomain and the identified sub-part, the digital destinationcorresponding to the particular network address represented by thesub-part of the domain; and performing an action based on the digitaldestination.
 9. The system of claim 8, wherein performing the actioncomprises displaying content associated with the digital destination.10. The system of claim 8, wherein performing the action comprisesexecuting an application based on data stored at the digitaldestination.
 11. The system of claim 8, wherein identifying the sub-partof the domain based on the textual content comprises decoding thetextual content.
 12. The system of claim 8, wherein identifying thedomain based on the graphical content comprises providing the graphicalcontent as an input to a machine learning model, wherein, in response tothe input, the machine learning model outputs a class identifying thedomain.
 13. The system of claim 12, wherein the machine learning modelis a convolutional neural network (CNN).
 14. The system of claim 8,wherein the object comprises at least one of a poster, billboard, sign,handwritten content, digital content, or receipt.
 15. One or morenon-transitory computer-readable media storing computer-executableinstructions that, when executed, cause one or more computing devicesto: capture an image of an object having a multi-part identifierdisplayed thereon, the multi-part identifier comprising a first portionand a second portion, the first portion comprising graphical content andthe second portion comprising human-recognizable textual content;identify a domain based on the graphical content, wherein the domainrepresents a network having a plurality of network addresses; identify asub-part of the domain based on the textual content, wherein thesub-part of the domain represents a particular network address in theplurality of network addresses; identify a digital destination based onthe identified domain and the identified sub-part, the digitaldestination corresponding to the particular network address representedby the sub-part of the domain; and perform an action based on thedigital destination.
 16. The one or more non-transitorycomputer-readable media of claim 15, wherein performing the actioncomprises at least one of displaying content associated with the digitaldestination or executing an application based on data stored at thedigital destination.
 17. The one or more non-transitorycomputer-readable media of claim 15, wherein identifying the sub-part ofthe domain based on the textual content comprises decoding the textualcontent.
 18. The one or more non-transitory computer-readable media ofclaim 15, wherein identifying the domain based on the graphical contentcomprises providing the graphical content as an input to a machinelearning model, wherein, in response to the input, the machine learningmodel outputs a class identifying the domain.
 19. The one or morenon-transitory computer-readable media of claim 18, wherein the machinelearning model is a convolutional neural network (CNN).
 20. The one ormore non-transitory computer-readable media of claim 15, wherein theobject comprises at least one of a poster, billboard, sign, handwrittencontent, digital content, or receipt.